Dynamic Data Fetching Control for On-Demand Database Systems
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Solution Overview
Problem
Conventional database systems face challenges in efficiently managing and retrieving large amounts of data without clogging the system or interrupting other queries, particularly in on-demand services environments where user interactions are increasing.
Innovation Solution
A mechanism for dynamically controlling data fetching at client computing devices, involving a server computing device that receives requests, matches them against predetermined threshold values, and generates multiple responses using formulas to optimize data retrieval, ensuring that large queries do not exceed system limits and maintain smooth execution of other queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If large amounts of data are retrieved from the backend system, then user data access completeness is improved, but system performance deteriorates due to clogging and query interruptions
Solution Approach 1:
The patent segments large data retrieval operations into smaller batches using pagination. The system divides complex queries into multiple smaller queries that retrieve data in manageable chunks (e.g., using OFFSET-LIMIT pagination or keyset pagination), preventing system clogging while ensuring complete data access over multiple retrieval cycles.
Solution Approach 2:
The patent implements dynamic query optimization by adjusting data retrieval strategies based on real-time system conditions. The system dynamically modifies query parameters, batch sizes, and retrieval frequencies based on current system load, available resources, and user behavior patterns to balance data completeness with system performance.
2Measurement precision
If complex queries are executed to retrieve comprehensive data, then data retrieval accuracy is improved, but query execution time increases and interrupts other queries
Solution Approach 1:
The patent applies preliminary actions by pre-compiling and caching query execution plans for commonly executed queries. The system analyzes query patterns and stores optimized execution strategies in advance, allowing rapid execution of accurate queries without re-parsing and optimization each time, thus reducing execution time while maintaining data retrieval accuracy.
Solution Approach 2:
The patent optimizes query parameters dynamically based on data distribution statistics and system state. The system adjusts parameters such as join strategies, index selection, and execution plans according to current data characteristics and resource availability, achieving accurate results faster by adapting to changing conditions rather than using fixed query approaches.
3Adaptability or versatility
If data fetching is increased to meet user expectations, then user satisfaction is improved, but heap memory limits are exceeded causing errors
Solution Approach 1:
The patent implements partial action by retrieving and processing data in controlled batches rather than loading all data at once. The system uses pagination mechanisms to fetch subsets of data, process them, and present results incrementally, satisfying user expectations for comprehensive data access while preventing heap memory exhaustion through controlled data loading.
Solution Approach 2:
The patent employs feedback mechanisms that monitor system resource usage during data retrieval operations. The system continuously tracks heap memory consumption, query performance metrics, and system load, dynamically adjusting data fetching strategies in response to observed conditions to prevent memory limit violations while maintaining user satisfaction.
Data Source
AI summary
In accordance with embodiments, there are provided mechanisms and methods for facilitating dynamically controlled fetching of data at client computing devices in an on-demand services environment in a multi-tenant environment according to one embodiment. In one embodiment and by way of example, a method includes receiving, at a server computing device, a request from a client computing device. The request includes a query for receiving data from a database coupled to the server computing device in an on-demand database system. The method may further include matching the request against a predetermine threshold value, and where the threshold value includes a first predetermined threshold range based on a first column range or a first row range and a second predetermined threshold range based on a second column range or a second row range. The method may further include generating multiple responses to the request based on one or more formulae and the predetermined threshold value if the request fails to satisfy the predetermined threshold value.